Identification of prognostic gene signatures of glioblastoma: a study based on TCGA data analysis.

ABSTRACT:

Background

The Cancer Genome Atlas (TCGA) project is a large-scale effort with the goal of identifying novel molecular aberrations in glioblastoma (GBM).

Methods

Here, we describe an in-depth analysis of gene expression data and copy number aberration (CNA) data to classify GBMs into prognostic groups to determine correlates of subtypes that may be biologically significant.

Results

To identify predictive survival models, we searched TCGA in 173 patients and identified 42 probe sets (P = .0005) that could be used to divide the tumor samples into 3 groups and showed a significantly (P = .0006) improved overall survival. Kaplan-Meier plots showed that the median survival of group 3 was markedly longer (127 weeks) than that of groups 1 and 2 (47 and 52 weeks, respectively

SUBMITTER: Kim YW 

PROVIDER: S-EPMC3688008 | biostudies-literature | 2013 Jul

REPOSITORIES: biostudies-literature